Living labs for in-situ open innovation: from idea to product validation and beyond

In this paper we present the Living Lab methodology as an overall framework for in-situ open innovation involving the end-user as equal participant in the innovation process. As a specific form of distributed innovation, relying on co-creation, we demonstrate the applicability of the Living Lab-approach for home ICT innovation by means of four innovation projects in different stages of maturity. We describe the used research methodologies and reflect on the role of the user

Video Recommendation Using Social Network Analysis and User Viewing Patterns

With the meteoric rise of video-on-demand (VOD) platforms, users face the challenge of sifting through an expansive sea of content to uncover shows that closely match their preferences. To address this information overload dilemma, VOD services have increasingly incorporated recommender systems powered by algorithms that analyze user behavior and suggest personalized content. However, a majority of existing recommender systems depend on explicit user feedback in the form of ratings and reviews, which can be difficult and time-consuming to collect at scale. This presents a key research gap, as leveraging users' implicit feedback patterns could provide an alternative avenue for building effective video recommendation models, circumventing the need for explicit ratings. However, prior literature lacks sufficient exploration into implicit feedback-based recommender systems, especially in the context of modeling video viewing behavior. Therefore, this paper aims to bridge this research gap by proposing a novel video recommendation technique that relies solely on users' implicit feedback in the form of their content viewing percentages

Promotion of active aging through a recommmmendation system based on multimedia content

Due to the increase in life expectancy, promotion of active aging has become a raising concern for human society. Machine Learning applications allow for dynamic and personalized solutions to support the chronic and complex healthcare challenges for elderly people. In particular, recommendation systems in the healthcare domain have shown positive results in the promotion of well being with non-intrusive methods. Considering how aging populations are some of the biggest consumers of television, there is an opportunity for recommendation systems specialized on that type of media to be used in the promotion of active aging. But existing systems in this context lack the ability to detect elderly users, which limits their usage to predetermined groups. This dissertation investigates the creation of an explainable recommendation system for television contents that can be used in the promotion of active aging. It also presents a method to detect older users from a dataset pertaining to television usage. The recommendation system was developed using both content-based and collaborative techniques, implemented with K-Nearest Neighbors (KNN) and Singular Value Decomposition (SVD) algorithms as well as cosine similarity. Explanations were proposed utilizing post-hoc and model-agnostic methods based on item and user similarity and evaluated with Mean Explainability Precision (MEP). The identification of elderly users was conducted with a clustering approach featuring Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Each of the explanation style that were used reflected a MEP value above 0.5 for both algorithms. The clustering from t-SNE allowed the identification of which division of the dataset was most likely to feature elderly users when compared to available statistics. These results reflect potential in application of the proposed system to an active aging context.Devido ao aumento da esperança média de vida, a promoção de envelhecimento ativo tem-se tornado uma preocupação crescente na sociedade humana. Algoritmos de aprendizagem automática permitem o desenvolvimento de soluções dinâmicas e personalizadas para o apoio dos desafios de saúde apresentados por pessoas idosas. Em destaque, sistemas de recomendação aplicados ao domínio da Saúde têm mostrado resultados positivos na promoção de bem-estar utilizando métodos não-intrusivos. Considerando como as populações envelhecidas são dos maiores consumidores de televisão, existe uma oportunidade para sistemas de recomendação especializados nesse tipo de media serem utilizados na promoção de envelhecimento ativo. No entanto, os sistemas existentes aplicáveis a este contexto não possuem a capacidade de detetar utilizadores idosos, o que limita a sua utilização a grupos predeterminados. Esta dissertação investiga a criação de um sistema de recomendação de conteúdos televisivos explicável que possa ser usado na promoção do envelhecimento ativo. Apresenta também um método para detetar utilizadores idosos de entre um conjunto de dados sobre visualizações de programas televisivos. O sistema de recomendação foi desenvolvido utilizando técnicas de filtragem colaborativa e baseadas no contéudo, implementadas com algoritmos de KNN e SVD, juntamente com semelhança de cosseno. Explicações foram propostas usando métodos post-hoc e de natureza agnóstica em relação aos algoritmos escolhidos, baseadas em semelhanças entre utilizadores e itens e avaliadas com MEP. A identificação de utilizadores idosos foi realizada com métodos de agrupamento de dados utilizando PCA e t-SNE. Cada estilo de explicação foi usado obteve um MEP superior a 0.5 para ambos os algoritmos. O agrupamento que recorreu a t-SNE permitiu distinguir em qual o grupo de utilizadores é mais provável existirem idosos através de comparações às estatísticas disponíveis. Estes resultados refletem o potencial na aplicação do sistema proposto ao contexto do envelhecimento ativo

Easy on that trigger dad: a study of long term family photo retrieval

We examine the effects of new technologies for digital photography on people's longer term storage and access to collections of personal photos. We report an empirical study of parents' ability to retrieve photos related to salient family events from more than a year ago. Performance was relatively poor with people failing to find almost 40% of pictures. We analyze participants' organizational and access strategies to identify reasons for this poor performance. Possible reasons for retrieval failure include: storing too many pictures, rudimentary organization, use of multiple storage systems, failure to maintain collections and participants' false beliefs about their ability to access photos. We conclude by exploring the technical and theoretical implications of these findings

Easy on that trigger dad: a study of long term family photo retrieval

We examine the effects of new technologies for digital photography on people's longer term storage and access to collections of personal photos. We report an empirical study of parents' ability to retrieve photos related to salient family events from more than a year ago. Performance was relatively poor with people failing to find almost 40% of pictures. We analyze participants' organizational and access strategies to identify reasons for this poor performance. Possible reasons for retrieval failure include: storing too many pictures, rudimentary organization, use of multiple storage systems, failure to maintain collections and participants' false beliefs about their ability to access photos. We conclude by exploring the technical and theoretical implications of these findings